
import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import AlignProcessor, EfficientNetImageProcessor

@require_vision
class 							_UpperCamelCase					(       unittest.TestCase	):






			'''simple docstring'''

			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							UpperCamelCase_:				Optional[int]						=    tempfile.mkdtemp()

							UpperCamelCase_:				Any						=    [
							    """[UNK]""",
							    """[CLS]""",
							    """[SEP]""",
							    """[PAD]""",
							    """[MASK]""",
							    """want""",
							    """##want""",
							    """##ed""",
							    """wa""",
							    """un""",
							    """runn""",
							    """##ing""",
							    """,""",
							    """low""",
							    """lowest""",
							]
							UpperCamelCase_:				Any						=    os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["""vocab_file"""]   )
							with open(self.vocab_file    ,						"""w"""    ,						encoding="""utf-8"""   ) as vocab_writer:
											vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]   )   )

							UpperCamelCase_:				Any						=    {
							    """do_resize""": True,
							    """size""": 20,
							    """do_center_crop""": True,
							    """crop_size""": 18,
							    """do_normalize""": True,
							    """image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
							    """image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
							}
							UpperCamelCase_:				List[str]						=    os.path.join(self.tmpdirname    ,						snake_case_   )
							with open(self.image_processor_file    ,						"""w"""    ,						encoding="""utf-8"""   ) as fp:
											json.dump(snake_case_    ,						snake_case_   )

			def    lowerCAmelCase__						(							self			:							Dict    ,						**snake_case_			:							List[str]   ):
							return BertTokenizer.from_pretrained(self.tmpdirname    ,						**snake_case_   )

			def    lowerCAmelCase__						(							self			:							int    ,						**snake_case_			:							Tuple   ):
							return BertTokenizerFast.from_pretrained(self.tmpdirname    ,						**snake_case_   )

			def    lowerCAmelCase__						(							self			:							Any    ,						**snake_case_			:							Tuple   ):
							return EfficientNetImageProcessor.from_pretrained(self.tmpdirname    ,						**snake_case_   )

			def    lowerCAmelCase__						(							self			:							List[Any]   ):
							shutil.rmtree(self.tmpdirname   )

			def    lowerCAmelCase__						(							self			:							Any   ):
							UpperCamelCase_:				str						=    [np.random.randint(255    ,						size=(3, 30, 400)    ,						dtype=np.uinta   )]
							UpperCamelCase_:				int						=    [Image.fromarray(np.moveaxis(snake_case_    ,						0    ,						-1   )   ) for x in image_inputs]
							return image_inputs

			def    lowerCAmelCase__						(							self			:							Tuple   ):
							UpperCamelCase_:				List[str]						=    self.get_tokenizer()
							UpperCamelCase_:				Dict						=    self.get_rust_tokenizer()
							UpperCamelCase_:				List[Any]						=    self.get_image_processor()

							UpperCamelCase_:				str						=    AlignProcessor(tokenizer=snake_case_    ,						image_processor=snake_case_   )
							processor_slow.save_pretrained(self.tmpdirname   )
							UpperCamelCase_:				Tuple						=    AlignProcessor.from_pretrained(self.tmpdirname    ,						use_fast=snake_case_   )

							UpperCamelCase_:				Any						=    AlignProcessor(tokenizer=snake_case_    ,						image_processor=snake_case_   )
							processor_fast.save_pretrained(self.tmpdirname   )
							UpperCamelCase_:				Optional[int]						=    AlignProcessor.from_pretrained(self.tmpdirname   )

							self.assertEqual(processor_slow.tokenizer.get_vocab()    ,						tokenizer_slow.get_vocab()   )
							self.assertEqual(processor_fast.tokenizer.get_vocab()    ,						tokenizer_fast.get_vocab()   )
							self.assertEqual(tokenizer_slow.get_vocab()    ,						tokenizer_fast.get_vocab()   )
							self.assertIsInstance(processor_slow.tokenizer    ,						snake_case_   )
							self.assertIsInstance(processor_fast.tokenizer    ,						snake_case_   )

							self.assertEqual(processor_slow.image_processor.to_json_string()    ,						image_processor.to_json_string()   )
							self.assertEqual(processor_fast.image_processor.to_json_string()    ,						image_processor.to_json_string()   )
							self.assertIsInstance(processor_slow.image_processor    ,						snake_case_   )
							self.assertIsInstance(processor_fast.image_processor    ,						snake_case_   )

			def    lowerCAmelCase__						(							self			:							Optional[int]   ):
							UpperCamelCase_:				Dict						=    AlignProcessor(tokenizer=self.get_tokenizer()    ,						image_processor=self.get_image_processor()   )
							processor.save_pretrained(self.tmpdirname   )

							UpperCamelCase_:				Optional[Any]						=    self.get_tokenizer(bos_token="""(BOS)"""    ,						eos_token="""(EOS)"""   )
							UpperCamelCase_:				Dict						=    self.get_image_processor(do_normalize=snake_case_    ,						padding_value=1.0   )

							UpperCamelCase_:				Optional[int]						=    AlignProcessor.from_pretrained(
							    self.tmpdirname    ,						bos_token="""(BOS)"""    ,						eos_token="""(EOS)"""    ,						do_normalize=snake_case_    ,						padding_value=1.0   )

							self.assertEqual(processor.tokenizer.get_vocab()    ,						tokenizer_add_kwargs.get_vocab()   )
							self.assertIsInstance(processor.tokenizer    ,						snake_case_   )

							self.assertEqual(processor.image_processor.to_json_string()    ,						image_processor_add_kwargs.to_json_string()   )
							self.assertIsInstance(processor.image_processor    ,						snake_case_   )

			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							UpperCamelCase_:				int						=    self.get_image_processor()
							UpperCamelCase_:				List[str]						=    self.get_tokenizer()

							UpperCamelCase_:				str						=    AlignProcessor(tokenizer=snake_case_    ,						image_processor=snake_case_   )

							UpperCamelCase_:				Tuple						=    self.prepare_image_inputs()

							UpperCamelCase_:				int						=    image_processor(snake_case_    ,						return_tensors="""np"""   )
							UpperCamelCase_:				str						=    processor(images=snake_case_    ,						return_tensors="""np"""   )

							for key in input_image_proc.keys():
											self.assertAlmostEqual(input_image_proc[key].sum()    ,						input_processor[key].sum()    ,						delta=1e-2   )

			def    lowerCAmelCase__						(							self			:							Optional[int]   ):
							UpperCamelCase_:				Union[str, Any]						=    self.get_image_processor()
							UpperCamelCase_:				Tuple						=    self.get_tokenizer()

							UpperCamelCase_:				List[Any]						=    AlignProcessor(tokenizer=snake_case_    ,						image_processor=snake_case_   )

							UpperCamelCase_:				Optional[int]						=    """lower newer"""

							UpperCamelCase_:				int						=    processor(text=snake_case_   )

							UpperCamelCase_:				List[str]						=    tokenizer(snake_case_    ,						padding="""max_length"""    ,						max_length=64   )

							for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]    ,						encoded_processor[key]   )

			def    lowerCAmelCase__						(							self			:							Optional[int]   ):
							UpperCamelCase_:				str						=    self.get_image_processor()
							UpperCamelCase_:				List[Any]						=    self.get_tokenizer()

							UpperCamelCase_:				Dict						=    AlignProcessor(tokenizer=snake_case_    ,						image_processor=snake_case_   )

							UpperCamelCase_:				Tuple						=    """lower newer"""
							UpperCamelCase_:				Dict						=    self.prepare_image_inputs()

							UpperCamelCase_:				List[str]						=    processor(text=snake_case_    ,						images=snake_case_   )

							self.assertListEqual(list(inputs.keys()   )    ,						["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]   )

							# test if it raises when no input is passed
							with pytest.raises(snake_case_   ):
											processor()

			def    lowerCAmelCase__						(							self			:							Optional[int]   ):
							UpperCamelCase_:				List[str]						=    self.get_image_processor()
							UpperCamelCase_:				List[str]						=    self.get_tokenizer()

							UpperCamelCase_:				List[Any]						=    AlignProcessor(tokenizer=snake_case_    ,						image_processor=snake_case_   )

							UpperCamelCase_:				Dict						=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							UpperCamelCase_:				Optional[int]						=    processor.batch_decode(snake_case_   )
							UpperCamelCase_:				List[Any]						=    tokenizer.batch_decode(snake_case_   )

							self.assertListEqual(snake_case_    ,						snake_case_   )




			def    lowerCAmelCase__						(							self			:							Optional[int]   ):
							UpperCamelCase_:				Tuple						=    self.get_image_processor()
							UpperCamelCase_:				Tuple						=    self.get_tokenizer()

							UpperCamelCase_:				Optional[Any]						=    AlignProcessor(tokenizer=snake_case_    ,						image_processor=snake_case_   )

							UpperCamelCase_:				Tuple						=    """lower newer"""
							UpperCamelCase_:				Optional[int]						=    self.prepare_image_inputs()

							UpperCamelCase_:				List[Any]						=    processor(text=snake_case_    ,						images=snake_case_   )

							self.assertListEqual(list(inputs.keys()   )    ,						processor.model_input_names   )








from __future__ import annotations

import time

lowerCamelCase_    :			Union[str, Any]    =	list[tuple[int, int]]

lowerCamelCase_    :			str    =	[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

lowerCamelCase_    :			Dict    =	[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

class 							_UpperCamelCase					:






			'''simple docstring'''

			def __init__(							self			:							Dict    ,						snake_case_			:							int    ,						snake_case_			:							int    ,						snake_case_			:							int    ,						snake_case_			:							int    ,						snake_case_			:							Node | None   ):
							UpperCamelCase_:				str						=    pos_x
							UpperCamelCase_:				List[str]						=    pos_y
							UpperCamelCase_:				str						=    (pos_y, pos_x)
							UpperCamelCase_:				Any						=    goal_x
							UpperCamelCase_:				Optional[int]						=    goal_y
							UpperCamelCase_:				Union[str, Any]						=    parent




class 							_UpperCamelCase					:






			'''simple docstring'''

			def __init__(							self			:							str    ,						snake_case_			:							tuple[int, int]    ,						snake_case_			:							tuple[int, int]   ):
							UpperCamelCase_:				Optional[Any]						=    Node(start[1]    ,						start[0]    ,						goal[1]    ,						goal[0]    ,						snake_case_   )
							UpperCamelCase_:				List[str]						=    Node(goal[1]    ,						goal[0]    ,						goal[1]    ,						goal[0]    ,						snake_case_   )

							UpperCamelCase_:				Any						=    [self.start]
							UpperCamelCase_:				Dict						=    False

			def    lowerCAmelCase__						(							self			:							str   ):
							while self.node_queue:
											UpperCamelCase_:				int						=    self.node_queue.pop(0   )

											if current_node.pos == self.target.pos:
															UpperCamelCase_:				List[str]						=    True
															return self.retrace_path(snake_case_   )

											UpperCamelCase_:				List[str]						=    self.get_successors(snake_case_   )

											for node in successors:
															self.node_queue.append(snake_case_   )

							if not self.reached:
											return [self.start.pos]
							return None

			def    lowerCAmelCase__						(							self			:							Dict    ,						snake_case_			:							Node   ):
							UpperCamelCase_:				int						=    []
							for action in delta:
											UpperCamelCase_:				Union[str, Any]						=    parent.pos_x + action[1]
											UpperCamelCase_:				str						=    parent.pos_y + action[0]
											if not (0 <= pos_x <= len(grid[0]   ) - 1 and 0 <= pos_y <= len(snake_case_   ) - 1):
															continue

											if grid[pos_y][pos_x] != 0:
															continue

											successors.append(
											    Node(snake_case_    ,						snake_case_    ,						self.target.pos_y    ,						self.target.pos_x    ,						snake_case_   )   )
							return successors




			def    lowerCAmelCase__						(							self			:							int    ,						snake_case_			:							Node | None   ):
							UpperCamelCase_:				int						=    node
							UpperCamelCase_:				Tuple						=    []
							while current_node is not None:
											path.append((current_node.pos_y, current_node.pos_x)   )
											UpperCamelCase_:				List[Any]						=    current_node.parent
							path.reverse()
							return path




class 							_UpperCamelCase					:






			'''simple docstring'''

			def __init__(							self			:							Optional[Any]    ,						snake_case_			:							Optional[Any]    ,						snake_case_			:							Optional[Any]   ):
							UpperCamelCase_:				Tuple						=    BreadthFirstSearch(snake_case_    ,						snake_case_   )
							UpperCamelCase_:				Dict						=    BreadthFirstSearch(snake_case_    ,						snake_case_   )
							UpperCamelCase_:				int						=    False

			def    lowerCAmelCase__						(							self			:							int   ):
							while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
											UpperCamelCase_:				List[Any]						=    self.fwd_bfs.node_queue.pop(0   )
											UpperCamelCase_:				Optional[Any]						=    self.bwd_bfs.node_queue.pop(0   )

											if current_bwd_node.pos == current_fwd_node.pos:
															UpperCamelCase_:				List[Any]						=    True
															return self.retrace_bidirectional_path(
															    snake_case_    ,						snake_case_   )

											UpperCamelCase_:				Optional[Any]						=    current_bwd_node
											UpperCamelCase_:				List[Any]						=    current_fwd_node

											UpperCamelCase_:				List[str]						=    {
											    self.fwd_bfs: self.fwd_bfs.get_successors(snake_case_   ),
											    self.bwd_bfs: self.bwd_bfs.get_successors(snake_case_   ),
											}

											for bfs in [self.fwd_bfs, self.bwd_bfs]:
															for node in successors[bfs]:
																			bfs.node_queue.append(snake_case_   )

							if not self.reached:
											return [self.fwd_bfs.start.pos]
							return None




			def    lowerCAmelCase__						(							self			:							List[str]    ,						snake_case_			:							Node    ,						snake_case_			:							Node   ):
							UpperCamelCase_:				List[str]						=    self.fwd_bfs.retrace_path(snake_case_   )
							UpperCamelCase_:				Tuple						=    self.bwd_bfs.retrace_path(snake_case_   )
							bwd_path.pop()
							bwd_path.reverse()
							UpperCamelCase_:				List[str]						=    fwd_path + bwd_path
							return path


if __name__ == "__main__":
							# all coordinates are given in format [y,x]
							import doctest

							doctest.testmod()
							lowerCamelCase_    :			Tuple    =	(0, 0)
							lowerCamelCase_    :			Union[str, Any]    =	(len(grid) - 1, len(grid[0]) - 1)
							for elem in grid:
														print(elem)

							lowerCamelCase_    :			Optional[int]    =	time.time()
							lowerCamelCase_    :			Tuple    =	BreadthFirstSearch(init, goal)
							lowerCamelCase_    :			List[str]    =	bfs.search()
							lowerCamelCase_    :			Optional[int]    =	time.time() - start_bfs_time

							print("""Unidirectional BFS computation time : """, bfs_time)

							lowerCamelCase_    :			Optional[int]    =	time.time()
							lowerCamelCase_    :			Optional[int]    =	BidirectionalBreadthFirstSearch(init, goal)
							lowerCamelCase_    :			str    =	bd_bfs.search()
							lowerCamelCase_    :			Tuple    =	time.time() - start_bd_bfs_time

							print("""Bidirectional BFS computation time : """, bd_bfs_time)






